Generic tensor completion methods such as Simple Low Rank Tensor Completion (SiLRTC) [1]} and High Accuracy Low Rank Tensor Completion (HaLRTC) [1]} were adapted to recover corrupted deep feature data packets in [3]}, [4]}, [5]}. Furthermore, [3]} proposed a method called ALTeC - Adaptive Linear Tensor Completion - specifically designed to recover missing intermediate features by pre-training on deep feature tensors. With a single-feature-row-per-packet scheme over a random loss channel, ALTeC proved to be as least as accurate as SiLRTC and HaLRTC while bringing a significant reduction in inference latency [3]}. In [4]}, a Content Adaptive Linear Tensor Completion (CALTeC) method was proposed to exploit existing redundancies in deep feature tensors. With a multiple-feature-rows-per-packet scheme over a Gilbert-Elliott channel, CALTeC and HaLRTC produced similar Top-1 classofication accuracy in [4]}. ALTeC slightly outperformed CALTeC and HaLRTC in one scenario and offered slightly worse performance than the other two in another scenario when the split point of the DNN was closer to its input and the number of feature rows per packet was increased. SiLRTC-completed tensors led to the poorest Top-1 classification accuracy in both scenarios in [4]}. In [5]}, PDE-based image inpainting methods, “Navier-Stokes" [12]} and “Telea" [13]}, performed lost packet concealment in collaborative object detection. Both inpainting methods led to higher mean average precision than SiLRTC with 250 iterations, while being more than 100\(\times \)  faster.
